Glyco-omics, thoughtfully connected.

library(glyread)

# Read pGlyco3 + pGlycoQuant output directly into
# a validated glyexp::GlycoproteomicSE container.
exp <- read_pglyco3_pglycoquant(
  "Quant.spectra.list",
  sample_info = "sample_info.csv",
  quant_method = "label-free",
  glycan_type = "N"
)

exp

library(glycoverse)

# Choose suitable preprocessing steps automatically.
clean_exp <- auto_clean(real_experiment)

# The same GlycoproteomicSE moves straight into models.
dea_res <- gly_limma(clean_exp)
pca_res <- gly_pca(clean_exp)

# Turn analysis results into publication-ready views.
plot_volcano(dea_res)
plot_pca(pca_res)

library(glyparse)
library(glymotif)

# auto_parse() recognizes common glycan text formats.
glycan <- auto_parse(
  "Gal(b1-3)GlcNAc(b1-3)Gal(b1-3)GalNAc(a1-"
)

# Query a curated motif by its biological name.
have_motif(glycan, "O-Glycan core 1")

library(glycoverse)
library(glyfun)

exp <- auto_clean(real_experiment)

# Conventional GO enrichment for dysregulated proteins.
dea_res <- gly_limma(exp)
go_res <- enrich_ora_go(dea_res)

# Or ask a glycan-centric question with derived traits.
trait_exp <- derive_traits(exp)
trait_dea <- gly_limma(trait_exp)
glycan_go <- enrich_gc_ora_go(trait_dea)

library(glyenzy)

glycan <- paste0(
  "Gal(b1-4)GlcNAc(b1-6)",
  "[Gal(b1-3)]GalNAc(a1-"
)

# Find enzymes in its biosynthesis...
find_enzyme(glycan)

# ...or predict products made by one enzyme.
apply_enzyme(glycan, "ST3GAL1")